3 accepted papers
Monocular video depth estimation (MVDE) plays a crucial role in 3D computer vision. In this paper, we propose an end-to-end monocular video depth estimation network based on temporal attention. Our network starts by a motion compensation module where the spatial temporal transformer network (STN) is…
We propose a novel mapping method to improve the training accuracy and efficiency of boosted classifiers for object detection. The key step of the proposed method is a non linear mapping on original samples by referring to the basis samples before feeding into the weak classifiers, where the basis…
In this paper, we propose a high-accuracy object detector based on co-occurrence features. Firstly, we introduce three kinds of local co-occurrence features constructed by the traditional Haar, LBP, and HOG respectively. Then the boosted detectors are learned, where each weak classifier corresponds…